topsis-102203958 1.0.7

A Python package to calculate TOPSIS scores

TOPSIS Package for Multi-Criteria Decision Making

What is TOPSIS?

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a widely used multi-criteria decision-making (MCDM) method. It ranks alternatives based on their closeness to an ideal solution and their distance from the worst solution.

About This Package

This package provides an easy-to-use implementation of the TOPSIS method for ranking alternatives. It processes a dataset in .csv or .xlsx format and outputs the original dataset with two additional columns: Topsis Score and Rank.

Features:

• Accepts both .csv and .xlsx input files.
• Automatically converts .xlsx to .csv if needed.
• Validates input data to ensure correctness.
• Handles weighted and impacted criteria for decision-making.

Installation

pip install topsis-102203958

Usage

This package is designed to run as a command-line utility.

Command Syntax:

python topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Parameters:

• <InputDataFile>: The input file containing the decision matrix (e.g., data.xlsx or data.csv).
• <Weights>: A comma-separated list of weights for the criteria (e.g., 1,1,1,2).
• <Impacts>: A comma-separated list of impacts (+ for maximization, - for minimization) (e.g., +,+,-,+).
• <ResultFileName>: The name of the output file (e.g., result.csv).

Example:

Input file data.xlsx:

| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 |
|-------|-------------|-------------|-------------|-------------|
| M1    | 250         | 16          | 12          | 5           |
| M2    | 200         | 20          | 15          | 8           |
| M3    | 300         | 18          | 10          | 6           |

Command:

python topsis data.xlsx "1,1,1,2" "+,+,-,+" result.csv

Output file result.csv:

| Model | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 | Topsis Score | Rank |
|-------|-------------|-------------|-------------|-------------|--------------|------|
| M1    | 250         | 16          | 12          | 5           | 0.672        | 2    |
| M2    | 200         | 20          | 15          | 8           | 0.432        | 3    |
| M3    | 300         | 18          | 10          | 6           | 0.789        | 1    |

Input File Requirements

• The file must contain at least three columns:
  • First column: Object/alternative names (e.g., M1, M2, M3).
  • Remaining columns: Numeric values only (criteria).
• If the input file is not .csv, it will be converted to 102203958-data.csv.
• Weights and impacts must match the number of criteria columns.

Error Handling

The package includes robust error handling for the following scenarios:

1. File Not Found: Displays an error if the input file does not exist.
2. Incorrect Parameters: Ensures the number of weights, impacts, and criteria columns match.
3. Non-Numeric Values: Verifies that all criteria columns contain numeric values only.
4. Invalid Impacts: Checks that impacts are either + or -.

License

This package is distributed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Feel free to fork the repository, make changes, and submit a pull request.

For major changes, please open an issue first to discuss what you would like to change.

Check out the repository here: GitHub Repository

Support

If you encounter any issues or have questions, please open an issue on the GitHub repository or contact the package maintainer.